The lattice gas automata for computational electromagnetics
Bibliographic record
Abstract
Time-domain analyses of electromagnetic phenomena such as the FDTD and TLM typically use as their starting point a set of differential equations derived from Maxwell's equations. Thus, our understanding of the latter phenomena has, in a certain sense, been limited to a realm in which one is forced to visualise abstract field quantities as these equations evolve mathematically. An alternative approach to modelling physical phenomena involves the use of the lattice gas automata. The lattice gas method utilizes the concept of a macroscopic observable emerging from interacting discrete particles on an extremely large fine-grain lattice. It is an approach often used in hydrodynamic modelling and in this context provides us with a more tangible mathematical representation of reality. In this paper, we employ the lattice gas approach and an analogy between acoustics and electromagnetics to investigate linear wave behaviour in two dimensions, as it applies to electromagnetics. In order to model dielectrics a variation of the HPP lattice gas automaton which includes the creation of rest particles is used. Low-cost, special purpose cellular automata machines, such as CAM-8, are invaluable computational resources for the evaluation of cellular automata. We have implemented our models on the CAM-8 and will demonstrate results obtained in the process.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".